A study of AAA image segmentation technique using geometric active contour model with morphological gradient edge function.

Abdominal aortic aneurysm (AAA) is a serious vascular disease that can be life threatening. Accurate measurement of AAA size is important for surgical or endovascular repair. We have examined the feasibility of using the proposed method to drive quantitative measurement of a region of interest from AAA. The proposed geometric active contour model (PGACM) is a modification of the conventional geometric active contour model (CGACM) that uses morphological gradient edge function rather than Gaussian filtered images. The rationale for this is to eliminate the blurring effect induced by the Gaussian filter in the CGACM. We used three noised synthetic images with different shapes. To test performance, three quantities that were normalized for minimum distance error, mismatched area, and execution time are evaluated. PGACM, parametric active contour model (PACM), and CGACM were compared with respect to the three quantities. With PGACM, we obtained better performance for the segmentation than with the PACM and CGACM. This study shows the feasibility, accuracy, and precision of segmentation of AAA from CT data, and indicates that the proposed method may be useful in patients with AAA.

Accessing heterogeneous sources of evidence to answer clinical questions.

The large and rapidly growing number of information sources relevant to health care, and the increasing amounts of new evidence produced by researchers, are improving the access of professionals and students to valuable information. However, seeking and filtering useful, valid information can be still very difficult. An online information system that conducts searches based on individual patient data can have a beneficial influence on the particular patient's outcome and educate the healthcare worker. In this paper, we describe the underlying model for a system that aims to facilitate the search for evidence based on clinicians' needs. This paper reviews studies of information needs of clinicians, describes principles of information retrieval, and examines the role that standardized terminologies can play in the integration between a clinical system and literature resources, as well as in the information retrieval process. The paper also describes a model for a digital library system that supports the integration of clinical systems with online information sources, making use of information available in the electronic medical record to enhance searches and information retrieval. The model builds on several different, previously developed techniques to identify information themes that are relevant to specific clinical data. Using a framework of evidence-based practice, the system generates well-structured questions with the intent of enhancing information retrieval. We believe that by helping clinicians to pose well-structured clinical queries and including in them relevant information from individual patients' medical records, we can enhance information retrieval and thus can improve patient-care.

Use of the Extensible Stylesheet Language (XSL) for medical data transformation.

Recently, the Extensible Markup Language (XML) has received growing attention as a simple but flexible mechanism to represent medical data. As XML-based markups become more common there will be an increasing need to transform data stored in one XML markup into another markup. The Extensible Stylesheet Language (XSL) is a stylesheet language for XML. Development of a new mammography reporting system created a need to convert XML output from the MEDLee natural language processing system into a format suitable for cross-patient reporting. This paper examines the capability of XSL as a rule specification language that supports the medical XML data transformation. A set of nine relevant transformations was identified: Filtering, Substitution, Specification, Aggregation, Merging, Splitting, Transposition, Push-down and Pull-up. XSL-based methods for implementing these transformations are presented. The strengths and limitations of XSL are discussed in the context of XML medical data transformation.